"""Workflow Optimizer - Integrate parallel processing optimization into existing workflows"""

import asyncio
import logging
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from enum import Enum

try:
    from .performance_optimizer import (
        AdvancedParallelExecutor,
    )
except ImportError:
    # Provide basic implementation if performance optimizer is not available
    class AdvancedParallelExecutor:
        def __init__(self, *args, **kwargs):
            pass

        async def start(self):
            pass

        async def stop(self):
            pass

        async def decompose_and_execute_research_task(self, query, workflow_type):
            return {"result": "Basic execution completed", "optimized": False}


logger = logging.getLogger(__name__)


class WorkflowOptimizationLevel(Enum):
    """Workflow optimization levels"""

    BASIC = "basic"  # Basic optimization
    STANDARD = "standard"  # Standard optimization
    ADVANCED = "advanced"  # Advanced optimization
    MAXIMUM = "maximum"  # Maximum optimization


@dataclass
class WorkflowOptimizationConfig:
    """Workflow optimization configuration"""

    optimization_level: WorkflowOptimizationLevel = WorkflowOptimizationLevel.STANDARD
    max_workers: int = 4
    enable_caching: bool = True
    enable_rate_limiting: bool = True
    enable_error_recovery: bool = True
    enable_metrics: bool = True
    timeout_seconds: float = 300.0


class WorkflowOptimizer:
    """Workflow Optimizer - Provide parallel processing optimization for existing workflows"""

    def __init__(self, config: Optional[WorkflowOptimizationConfig] = None):
        self.config = config or WorkflowOptimizationConfig()
        self.executor: Optional[AdvancedParallelExecutor] = None
        self.is_initialized = False

    async def initialize(self) -> bool:
        """Initialize optimizer"""
        try:
            if self.config.optimization_level in [
                WorkflowOptimizationLevel.ADVANCED,
                WorkflowOptimizationLevel.MAXIMUM,
            ]:
                self.executor = AdvancedParallelExecutor(
                    max_workers=self.config.max_workers,
                    enable_metrics=self.config.enable_metrics,
                )
                await self.executor.start()
                logger.info(
                    f"Advanced workflow optimizer initialized with {self.config.max_workers} workers"
                )
            else:
                logger.info("Basic workflow optimizer initialized")

            self.is_initialized = True
            return True

        except Exception as e:
            logger.error(f"Failed to initialize workflow optimizer: {e}")
            return False

    async def optimize_research_workflow(
        self,
        user_query: str,
        workflow_type: str = "research",
        context: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """Optimize research workflow"""
        if not self.is_initialized:
            await self.initialize()

        try:
            if self.executor and self.config.optimization_level in [
                WorkflowOptimizationLevel.ADVANCED,
                WorkflowOptimizationLevel.MAXIMUM,
            ]:
                # Use advanced parallel executor
                result = await self.executor.decompose_and_execute_research_task(
                    user_query, workflow_type
                )

                # Add optimization information
                result["optimization_applied"] = True
                result["optimization_level"] = self.config.optimization_level.value

                if self.config.enable_metrics:
                    metrics = await self.executor.get_metrics()
                    result["performance_metrics"] = metrics

                return result
            else:
                # Basic optimization - simple asynchronous execution
                return await self._basic_optimization(
                    user_query, workflow_type, context
                )

        except Exception as e:
            logger.error(f"Workflow optimization failed: {e}")
            return {
                "error": f"Optimization failed: {e}",
                "fallback_used": True,
                "optimization_applied": False,
            }

    async def _basic_optimization(
        self,
        user_query: str,
        workflow_type: str,
        context: Optional[Dict[str, Any]] = None,
    ) -> Dict[str, Any]:
        """Basic optimization implementation"""
        # Simulate basic parallel processing
        tasks = [
            self._simulate_research_task(f"Research aspect {i+1} of: {user_query}")
            for i in range(min(3, self.config.max_workers))
        ]

        results = await asyncio.gather(*tasks, return_exceptions=True)

        return {
            "results": [r for r in results if not isinstance(r, Exception)],
            "errors": [str(r) for r in results if isinstance(r, Exception)],
            "optimization_applied": True,
            "optimization_level": self.config.optimization_level.value,
            "workflow_type": workflow_type,
        }

    async def _simulate_research_task(self, task_description: str) -> Dict[str, Any]:
        """Simulate research task execution"""
        await asyncio.sleep(0.1)  # Simulate processing time
        return {
            "task": task_description,
            "status": "completed",
            "result": f"Processed: {task_description}",
        }

    async def optimize_report_generation(
        self, content_sections: List[str], report_type: str = "comprehensive"
    ) -> Dict[str, Any]:
        """Optimize report generation process"""
        if not self.is_initialized:
            await self.initialize()

        try:
            if self.executor and self.config.optimization_level in [
                WorkflowOptimizationLevel.ADVANCED,
                WorkflowOptimizationLevel.MAXIMUM,
            ]:
                # Process report sections in parallel
                tasks = []
                for i, section in enumerate(content_sections):
                    task = {
                        "id": f"report_section_{i}",
                        "function": self._generate_report_section,
                        "args": (section, report_type),
                        "priority": 2,  # Medium priority
                        "timeout": self.config.timeout_seconds / len(content_sections),
                    }
                    tasks.append(task)

                # Submit parallel tasks
                results = await self.executor.submit_parallel_tasks(tasks)

                return {
                    "sections": results,
                    "report_type": report_type,
                    "optimization_applied": True,
                    "sections_count": len(content_sections),
                }
            else:
                # Basic sequential processing
                sections = []
                for section in content_sections:
                    result = await self._generate_report_section(section, report_type)
                    sections.append(result)

                return {
                    "sections": sections,
                    "report_type": report_type,
                    "optimization_applied": False,
                }

        except Exception as e:
            logger.error(f"Report generation optimization failed: {e}")
            return {"error": f"Report optimization failed: {e}", "fallback_used": True}

    async def _generate_report_section(
        self, section_content: str, report_type: str
    ) -> Dict[str, Any]:
        """Generate report section"""
        # Simulate report section generation
        await asyncio.sleep(0.2)  # Simulate processing time

        return {
            "content": f"Generated {report_type} section: {section_content}",
            "word_count": len(section_content.split()) * 10,  # Simulate word count
            "generation_time": 0.2,
        }

    async def get_optimization_metrics(self) -> Dict[str, Any]:
        """Get optimization metrics"""
        if self.executor and self.config.enable_metrics:
            return await self.executor.get_metrics()
        else:
            return {
                "optimization_level": self.config.optimization_level.value,
                "max_workers": self.config.max_workers,
                "metrics_enabled": self.config.enable_metrics,
            }

    async def shutdown(self):
        """Shutdown optimizer"""
        if self.executor:
            await self.executor.stop()
            self.executor = None

        self.is_initialized = False
        logger.info("Workflow optimizer shutdown completed")


# Convenience functions
async def create_optimized_workflow(
    optimization_level: WorkflowOptimizationLevel = WorkflowOptimizationLevel.STANDARD,
    max_workers: int = 4,
) -> WorkflowOptimizer:
    """Create optimized workflow instance"""
    config = WorkflowOptimizationConfig(
        optimization_level=optimization_level, max_workers=max_workers
    )

    optimizer = WorkflowOptimizer(config)
    await optimizer.initialize()

    return optimizer


async def optimize_single_research_task(
    user_query: str, workflow_type: str = "research", max_workers: int = 4
) -> Dict[str, Any]:
    """Convenience function to optimize single research task"""
    optimizer = await create_optimized_workflow(
        optimization_level=WorkflowOptimizationLevel.ADVANCED, max_workers=max_workers
    )

    try:
        result = await optimizer.optimize_research_workflow(user_query, workflow_type)
        return result
    finally:
        await optimizer.shutdown()
